The course aims to explore the phenomenon of antisocial behavior in online communities and understand its underlying principles. Students will learn about data mining, crowdsourcing, and identifying online behavior patterns. The teaching method involves analyzing case studies and conducting multi-method analyses to predict and prevent negative behavior. This course is intended for individuals interested in social behavior, online communities, and building healthier online environments.
Overview
Syllabus
Introduction.
Understanding bad behavior helps us build healthier communities.
Antisocial behavior is largely due to ordinary people.
Data Mining + Crowdsourcing.
Identifying principles of online behavior.
CAN ANYONE BECOME A TROLL?.
What is trolling?.
Trolling is behavior that occurs outside community norms..
What if antisocial behavior is situational?.
Challenge: how to show that antisocial behavior is situational?.
Anyone can become a troll.
Initial seed posts in the negative context condition perceived worse.
How did trolling differ across conditions?.
Bad mood and negative discussion context increase trolling.
Can trolling, like mood, vary with the time of day and day of week?.
Mood spills over from prior discussions.
The initial post affects subsequent trolling.
Because trolling is situational, ordinary people can end up trolling.
Downvoting causes negative behavior to worsen.
Challenge: how to compare different users and posts?.
but doesn't change after a positive evaluation.
How does community bias change after an evaluation?.
Cascade growth is predictable.
Cascade structure is predictable.
Holistic approaches for analyzing and building social systems.
Multi-methods analyses identify patterns in data, verify hypotheses, make predictions, and inform the design of better social systems..
Taught by
Stanford Online
